1. Technical Field
This invention relates both to the art of machining and to the art of adaptive process control, and, more particularly, both to the art of finish-machining and the art of computer controlled adaptive processing.
2. Description of the Prior Art
The degree of surface roughness on machined parts is one of the most widely misunderstood and incorrectly specified aspects in part design. Reliability and optimum performance of the machined part are the primary reasons why selection of the proper machined surface is important. Surface roughness affects not only how a part fits and wears, but also how it may transmit heat, distribute a lubricant, accept a coating, or reflect light. Such part cannot possess the selected quality unless the surface roughness is controlled to be substantially the same on all the machined parts or segments. Without control, surface roughness will vary from part to part in a continuing machining process as a result of inherent variations in tool wear, tool composition, workpiece composition, and microflexing of the tool-holder to workpiece relationship; this will be true even though machining parameters such as feed, cut, and speed are kept constant.
Adaptive control of surface roughness would make it possible to achieve the benefits of precise specification of surface finish by assuring a substantially constant surface roughness throughout all the segments or parts of a given finish-machining operation. Adaptive control is used herein in a conventional manner to mean changing one or more of such machining parameters to influence surface roughness and maintain it at a desired level.
However, except for the activities of the inventors herein, adaptive control of surface roughness has not been undertaken by the prior art. Commerical machining operations today usually maintain the feed constant and adjust it only after the operation is stopped or after the machining run is complete, all in response to an off-line measurement. The use of the largest constant feasible depth of cut and the largest feed compatible with power and surface finish constraints is standard practice. Assuming adequate power is available, the surface finish desired determines the largest allowable constant feed. The constant feed is usually set conservatively low to ensure that the maximum allowable surface finish will not be exceeded as the tool wears. This, however, leads to poor productivity.
Adaptive controls were first used in the chemical industry to maintain a physical parameter at some desired level. Algorithms or computer models have been investigated to relate a selected parameter, such as pressure, to other influences that affect it such as temperature and reaction rate variables. An example is set forth in "Implementation of Self-Tuning Regulators", by T. Fortescue, L. Kershenbaum, and B. Ydstie, Automatica, Volume 17, No. 5 (1981), pp. 831-835. Such investigation worked with high order equations to devise dynamic math models that would accommodate a fast rate of data generation from the application. Estimates of the selected parameter were made on-line by recursive least squares estimation techniques, and as the estimates converged, control was achieved. Mathematical factors were inserted to keep the estimation techniques from imposing unstable control. Unfortunately, such computer model for the chemical industry involved too many variables and was much too complex to be used for straightforward control of surface roughness in a machining application. The question remains whether roughness can be mathematically related to essentially one variable: feed.
To answer this question, one must look to known adaptive controls in the machining art to see if a solution has been provided. Adaptive controls have been proposed for controlling aspects not directly related to surface roughness. Such controls are essentially of two types. One type is to optimize the least cost or time for machining by sensing a changing machining condition (i.e., tool wear) which cannot be totally controlled, and thence to use this information to adjust other machining parameters (i.e., speed and feed) to achieve cost or time optimization. When using feed and velocity, the feed and speed may be adjusted to obtain the most economical tool life.
References which disclose this first type of optimization control are: "Flank-Wear Model And Optimization Of Machining Process And its Control in Turning", by Y. Koren and J. Ben-Uri, Proc. Instn. Mech. Engrs., Volume 187, No. 25 (1973), pp. 301-307; "The Metal Cutting Optimal Control Problem--State Space Formulation", by E. Kannatey-Asibu, Computer Applications in Manufacturing Systems, ASME, 1981; "A Microprocessor Based Adaptive Control Of Machine Tools Using The Random Function Excursion Technique And Its Application to BTA Deep Hole Machining", by S. Chandrashekar, J. Frazao, T. Sankar, and H. Osman, Robotics and Computer Integrated Manufacturing, 1986; and "A Model-Based Approach To Adaptive Control Optimization In Milling", by T. Watanabe, ASME Journal of Dynamic Systems, Measurement and Control, Volume 108, March 1986, pp. 56-64.
The other type of adaptive machining control is to sense, in real time, a controllable machining condition (i.e., power consumption which can be controlled) and thence to use such sensed condition to change other machining conditions (i.e., speed and feed) to ensure that the first condition (power consumption) is constrained to be below a certain maximum level. References which disclose this type of constraint control are: "Adaptive Control With Process Estimation", by Koren et al, C.I.R.P. Annals, Volume 30, No. 1 (1981), pp. 373-376; "Experiments on Adaptive Constrained Control of a CNC Lathe", by R. Bedini and P. Pinotti, ASME Journal of Engineering for Industry, Volume 104, May 1982, pp. 139-150; "Adaptive Control In Machining-A New Approach Based On The Physical Constraints Of Tool Wear Mechanisms", by D. Yen and P. Wright, ASME Journal of Engineering for Industry, Volume 105, February 1983, pp. 31-38; and "Variable Gain Adaptive Control Systems For Machine Tools", by A. Ulsoy, Y. Koren, and L. Lauderbaugh, University of Michigan Technical Report No. UM-MEAM-83-18, October, 1983.
Optimization machining control, the first type, has not been used in the commercial machine tool industry because it requires on-line measurement of tool wear which has not yet been developed technically or economically to make it feasible. Constraint machining control, the second type, has been used only in commercial roughing operations; it is disadvantageous because it requires (i) the use of several expensive sensors to measure cutting forces, torque, or temperatures during machining, and (ii) extensive off-line acquisition of data to derive comparative computer models to establish maximum levels, all of which demand undue and expensive experimentation.
But, more importantly, both types of such adaptive controls in the machining arts are not designed to maximize workpiece quality, such as surface finish. None of such controls have entertained the idea of relating surface roughness to essentially only feed in a static relationship that reduces data gathering.
Accordingly, it is a primary object of this invention to utilize a mathematical model in such a way that it results in adjusting the machining feed to maintain a desired surface roughness for each machined part or segment in a series, despite tool wear, variations in tool material and geometry, and variability in workpiece composition. This will lead to improved and more uniform machining quality.
Another object of this invention is to provide such artificial intelligence for adaptively controlling feed which provides for machining workpieces at higher feeds and hence shorter cutting times than obtainable with conventional metal cutting operations. By automatically seeking higher feeds consistent with targeted surface finish and cutting tool conditions, increased productivity will indirectly result while maintaining consistent part quality.
Still another object of this invention is to provide an algorithm based on a simple static geometrical relationship that not only improves the predictability of a finish-machining process, but also permits the detection of a tool worn beyond its useful life; the latter detection is based on the rate of change of a specific coefficient of such algorithm. The algorithm would permit the controller to be self-learning and use the measured surface roughness to predict the condition of the cutting tool.